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Zhen-yu Yin


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Journal of Zhejiang University SCIENCE A 2020 Vol.21 No.6 P.407-411


Practice of artificial intelligence in geotechnical engineering

Author(s):  Zhen-yu Yin, Yin-fu Jin, Zhong-qiang Liu

Affiliation(s):  Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China; more

Corresponding email(s):   zhenyu.yin@polyu.edu.hk, yinfu.jin9019@gmail.com, zhongqiang.liu@ngi.no

Key Words:  Artificial intelligence, Geotechnical engineering, Big data

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Zhen-yu Yin, Yin-fu Jin, Zhong-qiang Liu. Practice of artificial intelligence in geotechnical engineering[J]. Journal of Zhejiang University Science A, 2020, 21(6): 407-411.

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geotechnical engineering deals with materials (e.g. soil and rock) that, by their very nature, exhibit varied and uncertain behavior due to the imprecise physical processes associated with their formation. Modeling the behavior of such materials in geotechnical engineering applications is complex and sometimes beyond the ability of most traditional forms of physically-based engineering methods. In recent years, the application of artificial intelligence (AI) in a wide range of geotechnical engineering has grown rapidly. AI can be very useful in solving problems where deterministic solutions are not available or are excessively expensive in terms of computational cost but for which there are significant observations and data available. Due to the nature of materials, geotechnical engineering deals with more uncertainties than other fields of civil and mechanical engineering. There is also much monitoring and site investigation data in geotechnical engineering which needs to be taken advantage of by using data analytic methods. Therefore, AI can be a suitable and effective alternative route to solving geotechnical engineering problems and significant developments have been made in recent years as much attention has been given to the area.


概要:岩土材料的复杂和不确定性致使传统理论在模拟和预测岩土工程问题经常显得无能为力. 近年来,随着人工智能和大数据技术的快速发展,人工智能技术在岩土工程领域有了广泛应用,例如岩土参数的优化智能识别和预测、边坡变形的长期预测、基坑开挖过程变形的实时监测和预测以及盾构隧道的变形和盾构机刀盘参数的预测和更新等. 为此,本专辑收集了在该研究领域具有代表性的研究成果,介绍了人工智能技术在岩土工程领域的进展和未来发展潜力,希望能帮助读者快速了解人工智能技术在岩土工程中的应用,以及推动岩土工程的智能化发展,为实现岩土工程智能化提供科学依据和技术支撑.
关键词:人工智能; 岩土工程; 大数据

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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